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- Shayle R. Searle

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
30
Citations
13,562
95
World Ranking
2185
National Ranking
921

1968 - Fellow of the American Statistical Association (ASA)

- Statistics
- Algebra
- Regression analysis

Shayle R. Searle mainly focuses on Statistics, Mixed model, Combinatorics, Linear model and Estimator. His Statistics research is multidisciplinary, incorporating perspectives in Matrix algebra and Selection. His Mixed model research includes elements of Generalized linear mixed model, Applied mathematics, Restricted maximum likelihood, Marginal model and Best linear unbiased prediction.

His Combinatorics study incorporates themes from Invertible matrix, Mathematical analysis, Matrix, Permutation matrix and Commutation matrix. His work carried out in the field of Linear model brings together such families of science as Minimum mean square error and Total least squares, Least squares, Generalized least squares, Non-linear least squares. His Estimator study combines topics in areas such as Law of total variance, One-way analysis of variance, Variance-based sensitivity analysis and Contrast.

- Generalized, Linear, and Mixed Models (2383 citations)
- Matrix Algebra Useful for Statistics (816 citations)
- Population Marginal Means in the Linear Model: An Alternative to Least Squares Means (687 citations)

His main research concerns Statistics, Applied mathematics, Variance components, Linear model and Econometrics. He conducted interdisciplinary study in his works that combined Statistics and Random effects model. His study on Generalized linear mixed model is often connected to Generalized inverse as part of broader study in Applied mathematics.

His Linear model study combines topics from a wide range of disciplines, such as Algorithm and Covariance matrix. In his study, Sire is inextricably linked to Herd, which falls within the broad field of Econometrics. The study incorporates disciplines such as Estimator and M-estimator in addition to Restricted maximum likelihood.

- Statistics (55.33%)
- Applied mathematics (18.67%)
- Variance components (16.67%)

- Statistics (55.33%)
- Econometrics (15.33%)
- Variance components (16.67%)

His scientific interests lie mostly in Statistics, Econometrics, Variance components, Algebra and Unbalanced data. In his research, Shayle R. Searle undertakes multidisciplinary study on Statistics and Random effects model. He combines subjects such as M-estimator, Linear model, Segmented regression, Maximum likelihood sequence estimation and Regression analysis with his study of Econometrics.

His Linear model research integrates issues from Linear predictor function, Covariance, Bayesian multivariate linear regression and Errors-in-variables models. His studies in Algebra integrate themes in fields like Discrete mathematics and Mathematical optimization. The Mixed model study combines topics in areas such as Numerical analysis and Statistical dispersion.

- General Linear Model (22 citations)
- Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) (5 citations)
- Prediction of Random Variables (4 citations)

- Statistics
- Algebra
- Regression analysis

Shayle R. Searle focuses on Statistics, Econometrics, Maximum likelihood, Proper linear model and Maximum likelihood sequence estimation. His study involves Regression analysis, Robust regression, Local regression, Linear regression and Bayesian multivariate linear regression, a branch of Statistics. The various areas that Shayle R. Searle examines in his Econometrics study include Errors-in-variables models, Linear model, Design matrix, Likelihood-ratio test and Likelihood function.

His Maximum likelihood research is multidisciplinary, relying on both One-way analysis of variance, Variance components, Variance function and Random variable. His research integrates issues of Generalized linear mixed model, Applied mathematics, Generalized linear array model, General linear model and Log-linear model in his study of Proper linear model. The concepts of his Maximum likelihood sequence estimation study are interwoven with issues in Score test, M-estimator, Restricted maximum likelihood and Expectation–maximization algorithm.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Generalized, Linear, and Mixed Models

Charles E. McCulloch;Shayle R. Searle.

**(2001)**

3768 Citations

Matrix Algebra Useful for Statistics

Shayle R. Searle.

**(1982)**

1275 Citations

Population Marginal Means in the Linear Model: An Alternative to Least Squares Means

S. R. Searle;F. M. Speed;G. A. Milliken.

The American Statistician **(1980)**

1258 Citations

On Deriving the Inverse of a Sum of Matrices

H. V. Henderson;S. R. Searle.

Siam Review **(1981)**

1037 Citations

Linear models for unbalanced data

S. R. Searle.

**(1987)**

936 Citations

The estimation of environmental and genetic trends from records subject to culling.

C. R. Henderson;O. Kempthorne;S. R. Searle;C. M. V. Krosigk.

Biometrics **(1959)**

652 Citations

Generalized Inverse Matrices

S. R. Searle.

Biometrics **(1971)**

593 Citations

Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model

R. R. Corbeil;S. R. Searle.

Technometrics **(1976)**

471 Citations

The Vec-Permutation Matrix, the Vec Operator and Kronecker Products: A Review

Harold V. Henderson;S. R. Searle.

Linear & Multilinear Algebra **(1981)**

411 Citations

Generalized, Linear, and Mixed Models: McCulloch/Generalized, Linear, and Mixed Models

Charles E. McCulloch;Shayle R. Searle.

**(2005)**

387 Citations

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